Upload README.md

README.md

@@ -1,3 +1,101 @@
 ---
 license: apache-2.0
+metrics:
+- accuracy
+- bleu
+pipeline_tag: text2text-generation
+tags:
+- chemistry
+- biology
+- medical
+- smiles
+- iupac
+- text-generation-inference
+widget:
+- text: CCO
+  example_title: ethanol
 ---
+# SMILES2IUPAC-canonical-small
+
+IUPAC2SMILES-canonical-small was designed to accurately translate IUPAC chemical names to SMILES. 
+
+## Model Details
+
+### Model Description
+
+IUPAC2SMILES-canonical-small is based on the MT5 model with optimizations in implementing different tokenizers for the encoder and decoder. 
+- **Developed by:** Knowladgator Engineering
+- **Model type:** Encoder-Decoder with attention mechanism
+- **Language(s) (NLP):** SMILES, IUPAC (English)
+- **License:** Apache License 2.0
+
+### Model Sources
+- **Paper:** coming soon
+- **Demo:** [ChemicalConverters](https://huggingface.co/spaces/knowledgator/ChemicalConverters)
+
+## Quickstart
+Firstly, install the library:
+```commandline
+pip install chemical-converters
+```
+### IUPAC to SMILES
+#### To perform simple translation, follow the example:
+```python
+from chemicalconverters import NamesConverter
+
+converter = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-base")
+print(converter.smiles_to_iupac('ethanol'))
+print(converter.smiles_to_iupac(['ethanol', 'ethanol', 'ethanol']))
+```
+```text
+['CCO']
+['CCO', 'CCO', 'CCO']
+```
+#### Processing in batches:
+```python
+from chemicalconverters import NamesConverter
+
+converter = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-base")
+print(converter.smiles_to_iupac(["buta-1,3-diene" for _ in range(10)], num_beams=1, 
+                                process_in_batch=True, batch_size=1000))
+```
+```text
+['<SYST>C=CC=C', '<SYST>C=CC=C'...]
+```
+Our models also predict IUPAC styles from the table:
+
+| Style Token | Description                                                                                        |
+|-------------|----------------------------------------------------------------------------------------------------|
+| `<BASE>`    | The most known name of the substance, sometimes is the mixture of traditional and systematic style |
+| `<SYST>`    | The totally systematic style without trivial names                                                 |
+| `<TRAD>`    | The style is based on trivial names of the parts of substances                                     |
+
+## Bias, Risks, and Limitations
+
+This model has limited accuracy in processing large molecules and currently, doesn't support isomeric and isotopic SMILES.
+
+### Training Procedure
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+The model was trained on 100M examples of SMILES-IUPAC pairs with lr=0.0003, batch_size=1024 for 2 epochs.
+
+## Evaluation
+
+| Model                               | Accuracy | BLEU-4 score | Size(MB) |
+|-------------------------------------|---------|------------------|----------|
+| IUPAC2SMILES-canonical-small        |88.9%    |0.966             |23        |
+| IUPAC2SMILES-canonical-base         |93.7%    |0.974             |180       |
+| STOUT V2.0\*                        |68.47%   |0.92              |128       |
+*According to the original paper https://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00512-4
+
+## Citation
+Coming soon.
+
+## Model Card Authors
+
+[Mykhailo Shtopko](https://huggingface.co/BioMike)
+
+## Model Card Contact
+
+[email protected]